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Showing posts older than #1320 · Back to latest

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Post #1319 803
📃A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions

📕 Journal:IEEE Transactions on Knowledge and Data Engineering (I.F.=8.9)
🗓
Publish year: 2025

🧑‍💻Authors: Zemin Liu; Yuan Li; Nan Chen, ...
🏢Universities: National University of Singapore

📎 Study the paper
📦 Github
💥 Early access

⚡️Channel: @ComplexNetworkAnalysis
#review #imbalanced #learning #graph
Post #1318 764

Forwarded from Bioinformatics

📄Graph neural networks for single-cell omics data: a review of approaches and applications

📙 Journal: Briefings in Bioinformatics (I.F.=6.8)
🗓 Publish year: 2025

🧑‍💻
Authors: Sijie Li, Heyang Hua, Shengquan Chen
🏢Universities: Nankai University, China

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📲Channel: @Bioinformatics
#review #gnn #single_cell #omic
Post #1317 718

Forwarded from Bioinformatics

📃 Biological Multi-Layer and Single Cell Network-Based Multiomics Models - a Review

🗓 Publish year: 2025

🧑‍💻
Authors: Marcello Barylli, Joyaditya Saha, Tineke E. Buffart , ...
🏢Universities: University of Amsterdam - Amsterdam University Medical Centers - Oncode Institute, Netherlands

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📲Channel: @Bioinformatics
#review #multiomics #network #multi_layer #single_cell
Post #1316 727
🎞 Introduction to Social Network Analysis


💥This session is part of the ESRC Centre for Society and Mental Health's Research Methods Primer and Provocation series.

💥In this session, Dr Molly Copeland and Holly Crudgington provide an introduction to social network analysis (SNA) with a focus on major theories and conceptual approaches to using ego-centric and sociometric network data for those new to considering networks.

📽 Watch

📱Channel: @ComplexNetworkAnalysis
#video
YouTube Introduction to Social Network Analysis This session is part of the ESRC Centre for Society and Mental Health's Research Methods Primer and Provocation series. In this session, Dr Molly Copeland and Holly Crudgington provide an introduction to social network analysis (SNA) with a focus on major…
Post #1315 689
📃Information diffusion analysis: process, model, deployment, and application

📗 Journal:The Knowledge Engineering Review (I.F.=2.8)
🗓
Publish year: 2025

🧑‍💻Authors: Shashank Sheshar Singh, Divya Srivastava, Madhushi Verma, ...
🏢Universities: Thapar Institute of Engineering & Technology, Bennett University, India

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⚡️Channel: @ComplexNetworkAnalysis
#review #explainability #gnn
Post #1308 729
📘 Introduction to Random Graphs
💥 Free online book by Carnegie Mellon University, 2025

🌐 Study

⚡️Channel: @ComplexNetworkAnalysis
#book #graph #random
Post #1307 759
📃Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook

🗓 Publish year: 2025

🧑‍💻Authors: Jiawei Xue, Ruichen Tan, Jianzhu Ma, Satish V. Ukkusuri

🏢Universities: Purdue University, West Lafayette, IN, USA.
Tsinghua University, Beijing, China.

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📱Channel: @ComplexNetworkAnalysis
#paper #Data_Mining #Transportation #GNN #review
Post #1306 718

Forwarded from Bioinformatics

📃 Graph Neural Network-Based Approaches to Drug Repurposing: A Comprehensive Survey

🗓 Publish year: 2025

🧑‍💻
Authors: Alireza A.Tabatabaei, Mohammad Ebrahim Mahdavi, Ehsan Beiranvand, ...
🏢Universities: University of Isfahan, Shahid Beheshti University of Medical Sciences, University of Tehran - Iran

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📲Channel: @Bioinformatics
#review #drug #repurposing #gnn
  • ❤ 1
Post #1305 863
📃A Review of Link Prediction Algorithms in Dynamic Networks

📗 Journal: Mathematics (I.F.=2.3)
🗓
Publish year: 2025

🧑‍💻Authors: Mengdi Sun, Minghu Tang
🏢Universities: Qinghai Minzu University, China

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⚡️Channel: @ComplexNetworkAnalysis
#review #explainability #gnn
  • 👍 1
Post #1304 845
🎞 Machine Learning with Graphs: GraphSAGE Neighbor Sampling

💥Free recorded course by Prof. Jure Leskovec

💥 This part discussed Neighbor Sampling, That is a representative method used to scale up GNNs to large graphs. The key insight is that a K-layer GNN generates a node embedding by using only the nodes from the K-hop neighborhood around that node. Therefore, to generate embeddings of nodes in the mini-batch, only the K-hop neighborhood nodes and their features are needed to load onto a GPU, a tractable operation even if the original graph is large. To further reduce the computational cost, only a subset of neighboring nodes is sampled for GNNs to aggregate.


📽 Watch

📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #GNN #Machine_Learning #GraphSAGE
YouTube Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 17.2 - GraphSAGE Neighbor Sampling For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3Brn5kW Lecture 17.2 - GraphSAGE Neighbor Sampling Scaling up GNNs Jure Leskovec Computer Science, PhD Neighbor Sampling is a representative…
Post #1302 757
📄 A Survey of Graph Transformers: Architectures, Theories and Applications

🗓 Publish year: 2025

🧑‍💻Authors: Chaohao Yuan, Kangfei Zhao, Ercan Engin Kuruoglu, ...
🏢Universities: Tsinghua University - Chinese University of Hong Kong - Chinese Academy of Sciences, China

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⚡️Channel: @ComplexNetworkAnalysis
#review #transformer
  • 👍 1
Post #1301 762
🎓 Algorithms and Graph Structures for Splitting Network Flows, in Theory and Practice

📕PhD thesis from University of Helsinki, Finland

🗓Publish year: 2025

📎 Study thesis

⚡️Channel: @ComplexNetworkAnalysis
#thesis #network_flow
Post #1300 778
📃Network link prediction via deep learning method: A comparative analysis with traditional methods

🗓 Publish year: 2024
📘
Journal: Engineering Science and Technology, an International Journal (I.F=5.1)

🧑‍💻Authors: Gholamreza Zare, Nima Jafari Navimipour, Mehdi Hosseinzadeh, Amir Sahafi

🏢Universities: Islamic Azad University, Qeshm Branch, Qeshm, Iran
Islamic Azad University, Tabriz Branch, Tabriz, Iran
National Yunlin University of Science and Technology, Douliou, Yunlin 64002, Taiwan
Western Caspian University, Baku, Azerbaijan
Duy Tan University, Da Nang, Viet Nam
Duy Tan University, School of Medicine and Pharmacy, Da Nang, Viet Nam
Islamic Azad University, South Tehran Branch, Tehran, Iran


📎 Study paper

📱Channel: @ComplexNetworkAnalysis
#paper #Link_Prediction #Deep_learning #traditional
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